{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Statistics for Data Scientists (Python)\n",
    "# Chapter 5. Classification\n",
    "> (c) 2019 Peter C. Bruce, Andrew Bruce, Peter Gedeck"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression #, LogisticRegressionCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support\n",
    "from sklearn.metrics import roc_curve, accuracy_score, roc_auc_score\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from imblearn.over_sampling import SMOTE, ADASYN, BorderlineSMOTE\n",
    "from pygam import LinearGAM, s, f, l\n",
    "\n",
    "\n",
    "from dmba import classificationSummary\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "DATA = Path('.').resolve().parents[1] / 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define paths to data sets. If you don't keep your data in the same directory as the code, adapt the path names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOAN3000_CSV = DATA / 'loan3000.csv'\n",
    "LOAN_DATA_CSV = DATA / 'loan_data.csv.gz'\n",
    "FULL_TRAIN_SET_CSV = DATA / 'full_train_set.csv.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes\n",
    "## The Naive Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted class:  default\n",
      "predicted probabilities\n",
      "    default  paid off\n",
      "0  0.653696  0.346304\n"
     ]
    }
   ],
   "source": [
    "loan_data = pd.read_csv(LOAN_DATA_CSV)\n",
    "\n",
    "# convert to categorical\n",
    "loan_data.outcome = loan_data.outcome.astype('category')\n",
    "loan_data.outcome.cat.reorder_categories(['paid off', 'default'])\n",
    "loan_data.purpose_ = loan_data.purpose_.astype('category')\n",
    "loan_data.home_ = loan_data.home_.astype('category')\n",
    "loan_data.emp_len_ = loan_data.emp_len_.astype('category')\n",
    "\n",
    "predictors = ['purpose_', 'home_', 'emp_len_']\n",
    "outcome = 'outcome'\n",
    "X = pd.get_dummies(loan_data[predictors], prefix='', prefix_sep='')\n",
    "y = loan_data[outcome]\n",
    "\n",
    "naive_model = MultinomialNB(alpha=0.01, fit_prior=True)\n",
    "naive_model.fit(X, y)\n",
    "\n",
    "new_loan = X.loc[146:146, :] \n",
    "print('predicted class: ', naive_model.predict(new_loan)[0])\n",
    "\n",
    "probabilities = pd.DataFrame(naive_model.predict_proba(new_loan),\n",
    "                             columns=naive_model.classes_)\n",
    "print('predicted probabilities',)\n",
    "print(probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example not in book"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical variables are not supported in scikit-learn. The example would need to demonstrate binning a variable and display the probability distribution of the bins.\n",
    "```\n",
    "## example not in book\n",
    "less_naive <- NaiveBayes(outcome ~ borrower_score + payment_inc_ratio + \n",
    "                           purpose_ + home_ + emp_len_, data = loan_data)\n",
    "less_naive$table[1:2]\n",
    "\n",
    "png(filename=file.path(PSDS_PATH, 'figures', 'psds_naive_bayes.png'),  width = 4, height=3, units='in', res=300)\n",
    "\n",
    "stats <- less_naive$table[[1]]\n",
    "ggplot(data.frame(borrower_score=c(0,1)), aes(borrower_score)) +\n",
    "  stat_function(fun = dnorm, color='blue', linetype=1, \n",
    "                arg=list(mean=stats[1, 1], sd=stats[1, 2])) +\n",
    "  stat_function(fun = dnorm, color='red', linetype=2, \n",
    "                arg=list(mean=stats[2, 1], sd=stats[2, 2])) +\n",
    "  labs(y='probability')\n",
    "dev.off()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminant Analysis\n",
    "## A Simple Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          0\n",
      "borrower_score     7.175839\n",
      "payment_inc_ratio -0.099676\n"
     ]
    }
   ],
   "source": [
    "loan3000 = pd.read_csv(LOAN3000_CSV)\n",
    "loan3000.outcome = loan3000.outcome.astype('category')\n",
    "\n",
    "predictors = ['borrower_score', 'payment_inc_ratio']\n",
    "outcome = 'outcome'\n",
    "\n",
    "X = loan3000[predictors]\n",
    "y = loan3000[outcome]\n",
    "\n",
    "loan_lda = LinearDiscriminantAnalysis()\n",
    "loan_lda.fit(X, y)\n",
    "print(pd.DataFrame(loan_lda.scalings_, index=X.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    default  paid off\n",
      "0  0.553544  0.446456\n",
      "1  0.558953  0.441047\n",
      "2  0.272696  0.727304\n",
      "3  0.506254  0.493746\n",
      "4  0.609952  0.390048\n"
     ]
    }
   ],
   "source": [
    "pred = pd.DataFrame(loan_lda.predict_proba(loan3000[predictors]),\n",
    "                    columns=loan_lda.classes_)\n",
    "print(pred.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use scalings and center of means to determine decision boundary\n",
    "center = np.mean(loan_lda.means_, axis=0)\n",
    "slope = - loan_lda.scalings_[0] / loan_lda.scalings_[1]\n",
    "intercept = center[1] - center[0] * slope\n",
    "\n",
    "# payment_inc_ratio for borrower_score of 0 and 20\n",
    "x_0 = (0 - intercept) / slope\n",
    "x_20 = (20 - intercept) / slope\n",
    "\n",
    "lda_df = pd.concat([loan3000, pred['default']], axis=1)\n",
    "lda_df.head()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "g = sns.scatterplot(x='borrower_score', y='payment_inc_ratio',\n",
    "                    hue='default', data=lda_df, \n",
    "                    palette=sns.diverging_palette(240, 10, n=9, as_cmap=True),\n",
    "                    ax=ax, legend=False)\n",
    "\n",
    "ax.set_ylim(0, 20)\n",
    "ax.set_xlim(0.15, 0.8)\n",
    "ax.plot((x_0, x_20), (0, 20), linewidth=3)\n",
    "ax.plot(*loan_lda.means_.transpose())\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression\n",
    "## Logistic Response Function and Logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = np.arange(0.01, 1, 0.01)\n",
    "df = pd.DataFrame({\n",
    "    'p': p,\n",
    "    'logit': np.log(p / (1 - p)),\n",
    "    'odds': p / (1 - p),\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "ax.axhline(0, color='grey', linestyle='--')\n",
    "ax.axvline(0.5, color='grey', linestyle='--')\n",
    "ax.plot(df['p'], df['logit'])\n",
    "ax.set_xlabel('Probability')\n",
    "ax.set_ylabel('logit(p)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression and the GLM\n",
    "The package _scikit-learn_ has a specialised class for `LogisticRegression`. _Statsmodels_ has a more general method based on generalized linear model (GLM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  -1.6380884395576985\n",
      "classes ['default' 'paid off']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <td>-0.079728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>borrower_score</th>\n",
       "      <td>4.611037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_consolidation</th>\n",
       "      <td>-0.249342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_improvement</th>\n",
       "      <td>-0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_purchase</th>\n",
       "      <td>-0.229376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical</th>\n",
       "      <td>-0.510087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>-0.620534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small_business</th>\n",
       "      <td>-1.215662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>-0.048453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>-0.157355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt; 1 Year</th>\n",
       "      <td>0.357463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       coeff\n",
       "payment_inc_ratio  -0.079728\n",
       "borrower_score      4.611037\n",
       "debt_consolidation -0.249342\n",
       "home_improvement   -0.407614\n",
       "major_purchase     -0.229376\n",
       "medical            -0.510087\n",
       "other              -0.620534\n",
       "small_business     -1.215662\n",
       "OWN                -0.048453\n",
       "RENT               -0.157355\n",
       " > 1 Year           0.357463"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = ['payment_inc_ratio', 'purpose_', 'home_', 'emp_len_', \n",
    "              'borrower_score']\n",
    "outcome = 'outcome'\n",
    "X = pd.get_dummies(loan_data[predictors], prefix='', prefix_sep='', \n",
    "                   drop_first=True)\n",
    "y = loan_data[outcome] # .cat.categories\n",
    "\n",
    "logit_reg = LogisticRegression(penalty='l2', C=1e42, solver='liblinear')\n",
    "logit_reg.fit(X, y)\n",
    "\n",
    "print('intercept ', logit_reg.intercept_[0])\n",
    "print('classes', logit_reg.classes_)\n",
    "pd.DataFrame({'coeff': logit_reg.coef_[0]}, \n",
    "             index=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the intercept and coefficients are reversed compared to the R model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['credit_card', 'debt_consolidation', 'home_improvement',\n",
      "       'major_purchase', 'medical', 'other', 'small_business'],\n",
      "      dtype='object')\n",
      "Index(['MORTGAGE', 'OWN', 'RENT'], dtype='object')\n",
      "Index([' < 1 Year', ' > 1 Year'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(loan_data['purpose_'].cat.categories)\n",
    "print(loan_data['home_'].cat.categories)\n",
    "print(loan_data['emp_len_'].cat.categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Not in book_ :\n",
    "If you have a feature or outcome variable that is ordinal, use the scikit-learn `OrdinalEncoder` to replace the categories (here, 'paid off' and 'default') with numbers. In the below code, we replace 'paid off' with 0 and 'default' with 1. This reverses the order of the predicted classes and as a consequence, the coefficients will be reversed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept  1.638088492616834\n",
      "classes [0. 1.]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>payment_inc_ratio</th>\n",
       "      <td>0.079728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>borrower_score</th>\n",
       "      <td>-4.611037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_consolidation</th>\n",
       "      <td>0.249342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_improvement</th>\n",
       "      <td>0.407614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major_purchase</th>\n",
       "      <td>0.229376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical</th>\n",
       "      <td>0.510087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>0.620534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small_business</th>\n",
       "      <td>1.215662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>0.048453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>0.157355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt; 1 Year</th>\n",
       "      <td>-0.357463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       coeff\n",
       "payment_inc_ratio   0.079728\n",
       "borrower_score     -4.611037\n",
       "debt_consolidation  0.249342\n",
       "home_improvement    0.407614\n",
       "major_purchase      0.229376\n",
       "medical             0.510087\n",
       "other               0.620534\n",
       "small_business      1.215662\n",
       "OWN                 0.048453\n",
       "RENT                0.157355\n",
       " > 1 Year          -0.357463"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "enc = OrdinalEncoder(categories=[['paid off', 'default']])\n",
    "y_enc = enc.fit_transform(loan_data[[outcome]]).ravel()\n",
    "\n",
    "logit_reg_enc = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "logit_reg_enc.fit(X, y_enc)\n",
    "\n",
    "print('intercept ', logit_reg_enc.intercept_[0])\n",
    "print('classes', logit_reg_enc.classes_)\n",
    "pd.DataFrame({'coeff': logit_reg_enc.coef_[0]}, \n",
    "             index=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicted Values from Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            default      paid off\n",
      "count  45342.000000  45342.000000\n",
      "mean      -0.757850     -0.760423\n",
      "std        0.378032      0.390419\n",
      "min       -2.768873     -3.538864\n",
      "25%       -0.985728     -0.977164\n",
      "50%       -0.697366     -0.688946\n",
      "75%       -0.472209     -0.467076\n",
      "max       -0.029476     -0.064787\n"
     ]
    }
   ],
   "source": [
    "pred = pd.DataFrame(logit_reg.predict_log_proba(X),\n",
    "                    columns=logit_reg.classes_)\n",
    "print(pred.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            default      paid off\n",
      "count  45342.000000  45342.000000\n",
      "mean       0.500001      0.499999\n",
      "std        0.167336      0.167336\n",
      "min        0.062733      0.029046\n",
      "25%        0.373167      0.376377\n",
      "50%        0.497895      0.502105\n",
      "75%        0.623623      0.626833\n",
      "max        0.970954      0.937267\n"
     ]
    }
   ],
   "source": [
    "pred = pd.DataFrame(logit_reg.predict_proba(X),\n",
    "                    columns=logit_reg.classes_)\n",
    "print(pred.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting the Coefficients and Odds Ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3, 3))\n",
    "ax.plot(df['logit'], df['odds'])\n",
    "ax.set_xlabel('log(odds ratio)')\n",
    "ax.set_ylabel('odds ratio')\n",
    "ax.set_xlim(0, 5.1)\n",
    "ax.set_ylim(-5, 105)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing the Model\n",
    "For comparison, here the GLM model using _statsmodels_. This method requires that the outcome is mapped to numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                45342\n",
      "Model:                            GLM   Df Residuals:                    45330\n",
      "Model Family:                Binomial   Df Model:                           11\n",
      "Link Function:                  logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -28757.\n",
      "Date:                Tue, 28 Jul 2020   Deviance:                       57515.\n",
      "Time:                        15:48:33   Pearson chi2:                 4.54e+04\n",
      "No. Iterations:                     4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "payment_inc_ratio      0.0797      0.002     32.058      0.000       0.075       0.085\n",
      "borrower_score        -4.6126      0.084    -55.203      0.000      -4.776      -4.449\n",
      "debt_consolidation     0.2494      0.028      9.030      0.000       0.195       0.303\n",
      "home_improvement       0.4077      0.047      8.747      0.000       0.316       0.499\n",
      "major_purchase         0.2296      0.054      4.277      0.000       0.124       0.335\n",
      "medical                0.5105      0.087      5.882      0.000       0.340       0.681\n",
      "other                  0.6207      0.039     15.738      0.000       0.543       0.698\n",
      "small_business         1.2153      0.063     19.192      0.000       1.091       1.339\n",
      "OWN                    0.0483      0.038      1.271      0.204      -0.026       0.123\n",
      "RENT                   0.1573      0.021      7.420      0.000       0.116       0.199\n",
      " > 1 Year             -0.3567      0.053     -6.779      0.000      -0.460      -0.254\n",
      "const                  1.6381      0.074     22.224      0.000       1.494       1.783\n",
      "======================================================================================\n"
     ]
    }
   ],
   "source": [
    "# use GLM (general linear model) with the binomial family to \n",
    "# fit a logistic regression\n",
    "y_numbers = [1 if yi == 'default' else 0 for yi in y]\n",
    "logit_reg_sm = sm.GLM(y_numbers, X.assign(const=1), \n",
    "                      family=sm.families.Binomial())\n",
    "logit_result = logit_reg_sm.fit()\n",
    "print(logit_result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use splines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Generalized Linear Model Regression Results                             \n",
      "=====================================================================================================\n",
      "Dep. Variable:     ['outcome[default]', 'outcome[paid off]']   No. Observations:                45342\n",
      "Model:                                                   GLM   Df Residuals:                    45321\n",
      "Model Family:                                       Binomial   Df Model:                           20\n",
      "Link Function:                                         logit   Scale:                          1.0000\n",
      "Method:                                                 IRLS   Log-Likelihood:                -28731.\n",
      "Date:                                       Tue, 28 Jul 2020   Deviance:                       57462.\n",
      "Time:                                               15:48:34   Pearson chi2:                 4.54e+04\n",
      "No. Iterations:                                            6                                         \n",
      "Covariance Type:                                   nonrobust                                         \n",
      "==================================================================================================\n",
      "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------\n",
      "Intercept                          1.5756      0.331      4.765      0.000       0.928       2.224\n",
      "purpose_[T.debt_consolidation]     0.2486      0.028      8.998      0.000       0.194       0.303\n",
      "purpose_[T.home_improvement]       0.4097      0.047      8.757      0.000       0.318       0.501\n",
      "purpose_[T.major_purchase]         0.2382      0.054      4.416      0.000       0.132       0.344\n",
      "purpose_[T.medical]                0.5206      0.087      5.980      0.000       0.350       0.691\n",
      "purpose_[T.other]                  0.6284      0.040     15.781      0.000       0.550       0.706\n",
      "purpose_[T.small_business]         1.2250      0.063     19.305      0.000       1.101       1.349\n",
      "home_[T.OWN]                       0.0498      0.038      1.309      0.191      -0.025       0.124\n",
      "home_[T.RENT]                      0.1577      0.021      7.431      0.000       0.116       0.199\n",
      "emp_len_[T. > 1 Year]             -0.3526      0.053     -6.699      0.000      -0.456      -0.249\n",
      "bs(payment_inc_ratio, df=8)[0]     0.7042      0.342      2.060      0.039       0.034       1.374\n",
      "bs(payment_inc_ratio, df=8)[1]     0.6621      0.198      3.351      0.001       0.275       1.049\n",
      "bs(payment_inc_ratio, df=8)[2]     0.8118      0.245      3.309      0.001       0.331       1.293\n",
      "bs(payment_inc_ratio, df=8)[3]     1.0377      0.223      4.644      0.000       0.600       1.476\n",
      "bs(payment_inc_ratio, df=8)[4]     1.1901      0.233      5.112      0.000       0.734       1.646\n",
      "bs(payment_inc_ratio, df=8)[5]     2.8404      0.316      8.980      0.000       2.220       3.460\n",
      "bs(payment_inc_ratio, df=8)[6]    -1.3427      1.229     -1.092      0.275      -3.752       1.067\n",
      "bs(payment_inc_ratio, df=8)[7]     7.1094      6.393      1.112      0.266      -5.420      19.639\n",
      "bs(borrower_score, df=3)[0]       -2.9011      0.533     -5.448      0.000      -3.945      -1.857\n",
      "bs(borrower_score, df=3)[1]       -2.6056      0.196    -13.284      0.000      -2.990      -2.221\n",
      "bs(borrower_score, df=3)[2]       -5.7421      0.508    -11.313      0.000      -6.737      -4.747\n",
      "==================================================================================================\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "formula = ('outcome ~ bs(payment_inc_ratio, df=8) + purpose_ + ' +\n",
    "           'home_ + emp_len_ + bs(borrower_score, df=3)')\n",
    "model = smf.glm(formula=formula, data=loan_data, family=sm.families.Binomial())\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.genmod.generalized_linear_model import GLMResults\n",
    "def partialResidualPlot(model, df, outcome, feature, fig, ax):\n",
    "    y_actual = [0 if s == 'default' else 1 for s in df[outcome]]\n",
    "    y_pred = model.predict(df)\n",
    "    org_params = model.params.copy()\n",
    "    zero_params = model.params.copy()\n",
    "    # set model parametes of other features to 0\n",
    "    for i, name in enumerate(zero_params.index):\n",
    "        if feature in name:\n",
    "            continue\n",
    "        zero_params[i] = 0.0\n",
    "    model.initialize(model.model, zero_params)\n",
    "    feature_prediction = model.predict(df)\n",
    "    ypartial = -np.log(1/feature_prediction - 1)\n",
    "    ypartial = ypartial - np.mean(ypartial)\n",
    "    model.initialize(model.model, org_params)\n",
    "    results = pd.DataFrame({\n",
    "        'feature': df[feature],\n",
    "        'residual': -2 * (y_actual - y_pred),\n",
    "        'ypartial': ypartial/ 2,\n",
    "    })\n",
    "    results = results.sort_values(by=['feature'])\n",
    "\n",
    "    ax.scatter(results.feature, results.residual, marker=\".\", s=72./fig.dpi)\n",
    "    ax.plot(results.feature, results.ypartial, color='black')\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel(f'Residual + {feature} contribution')\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "partialResidualPlot(results, loan_data, 'outcome', 'payment_inc_ratio', fig, ax)\n",
    "ax.set_xlim(0, 25)\n",
    "ax.set_ylim(-2.5, 2.5)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Classification Models\n",
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Yhat = default  Yhat = paid off\n",
      "Y = default            14336             8335\n",
      "Y = paid off            8148            14523\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "pred = logit_reg.predict(X)\n",
    "pred_y = logit_reg.predict(X) == 'default'\n",
    "true_y = y == 'default'\n",
    "true_pos = true_y & pred_y\n",
    "true_neg = ~true_y & ~pred_y\n",
    "false_pos = ~true_y & pred_y\n",
    "false_neg = true_y & ~pred_y\n",
    "\n",
    "conf_mat = pd.DataFrame([[np.sum(true_pos), np.sum(false_neg)], [np.sum(false_pos), np.sum(true_neg)]],\n",
    "                       index=['Y = default', 'Y = paid off'],\n",
    "                       columns=['Yhat = default', 'Yhat = paid off'])\n",
    "print(conf_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14336  8335]\n",
      " [ 8148 14523]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y, logit_reg.predict(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The package _dmba_ contains the function `classificationSummary` that prints confusion matrix and accuracy for a classification model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix (Accuracy 0.6365)\n",
      "\n",
      "         Prediction\n",
      "  Actual  default paid off\n",
      " default    14336     8335\n",
      "paid off     8148    14523\n"
     ]
    }
   ],
   "source": [
    "classificationSummary(y, logit_reg.predict(X), \n",
    "                      class_names=logit_reg.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision, Recall, and Specificity\n",
    "The _scikit-learn_ function `precision_recall_fscore_support` returns\n",
    "precision, recall, fbeta_score and support. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision 0.6376089663760897\n",
      "Recall 0.6323496978518812\n",
      "Specificity 0.6405981209474659\n"
     ]
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(y, logit_reg.predict(X))\n",
    "print('Precision', conf_mat[0, 0] / sum(conf_mat[:, 0]))\n",
    "print('Recall', conf_mat[0, 0] / sum(conf_mat[0, :]))\n",
    "print('Specificity', conf_mat[1, 1] / sum(conf_mat[1, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.63760897, 0.63535742]),\n",
       " array([0.6323497 , 0.64059812]),\n",
       " array([0.63496844, 0.63796701]),\n",
       " array([22671, 22671]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_recall_fscore_support(y, logit_reg.predict(X), \n",
    "                                labels=['default', 'paid off'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve\n",
    "The function `roc_curve` in _Scikit-learn_ calculates all the information that is required for plotting a ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y, logit_reg.predict_proba(X)[:, 0], \n",
    "                                 pos_label='default')\n",
    "roc_df = pd.DataFrame({'recall': tpr, 'specificity': 1 - fpr})\n",
    "\n",
    "ax = roc_df.plot(x='specificity', y='recall', figsize=(4, 4), legend=False)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_xlim(1, 0)\n",
    "ax.plot((1, 0), (0, 1))\n",
    "ax.set_xlabel('specificity')\n",
    "ax.set_ylabel('recall')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AUC\n",
    "Accuracy can easily be calculated using the _scikit-learn_ function `accuracy_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6917107913974234\n",
      "0.6917108692223352\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(roc_df.recall[:-1] * np.diff(1 - roc_df.specificity)))\n",
    "print(roc_auc_score([1 if yi == 'default' else 0 for yi in y], logit_reg.predict_proba(X)[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y, logit_reg.predict_proba(X)[:,0], \n",
    "                                 pos_label='default')\n",
    "roc_df = pd.DataFrame({'recall': tpr, 'specificity': 1 - fpr})\n",
    "\n",
    "ax = roc_df.plot(x='specificity', y='recall', figsize=(4, 4), legend=False)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_xlim(1, 0)\n",
    "# ax.plot((1, 0), (0, 1))\n",
    "ax.set_xlabel('specificity')\n",
    "ax.set_ylabel('recall')\n",
    "ax.fill_between(roc_df.specificity, 0, roc_df.recall, alpha=0.3)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strategies for Imbalanced Data\n",
    "## Undersampling\n",
    "> The results differ from the R version, however are equivalent to results obtained using the R code. Model based results are of similar magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(119987, 19)\n"
     ]
    }
   ],
   "source": [
    "full_train_set = pd.read_csv(FULL_TRAIN_SET_CSV)\n",
    "print(full_train_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.894546909248504\n",
      "percentage of loans in default:  None\n"
     ]
    }
   ],
   "source": [
    "print('percentage of loans in default: ', \n",
    "print(      100 * np.mean(full_train_set.outcome == 'default')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.76591630759999\n",
      "percentage of loans predicted to default:  None\n"
     ]
    }
   ],
   "source": [
    "predictors = ['payment_inc_ratio', 'purpose_', 'home_', 'emp_len_', \n",
    "              'dti', 'revol_bal', 'revol_util']\n",
    "outcome = 'outcome'\n",
    "X = pd.get_dummies(full_train_set[predictors], prefix='', prefix_sep='', \n",
    "                   drop_first=True)\n",
    "y = full_train_set[outcome]\n",
    "\n",
    "full_model = LogisticRegression(penalty='l2', C=1e42, solver='liblinear')\n",
    "full_model.fit(X, y)\n",
    "print('percentage of loans predicted to default: ', \n",
    "print(      100 * np.mean(full_model.predict(X) == 'default')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.669205658324266"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.mean(full_train_set.outcome == 'default') / \n",
    " np.mean(full_model.predict(X) == 'default'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oversampling and Up/Down Weighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.58250477135023\n",
      "percentage of loans predicted to default (weighting):  None\n"
     ]
    }
   ],
   "source": [
    "default_wt = 1 / np.mean(full_train_set.outcome == 'default')\n",
    "wt = [default_wt if outcome == 'default' else 1 for outcome in full_train_set.outcome]\n",
    "\n",
    "full_model = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "full_model.fit(X, y, wt)\n",
    "print('percentage of loans predicted to default (weighting): ', \n",
    "print(      100 * np.mean(full_model.predict(X) == 'default')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Generation\n",
    "The package _imbalanced-learn_ provides an implementation of the _SMOTE_ and similar algorithms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of loans in default (SMOTE resampled):  50.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of loans predicted to default (SMOTE):  29.43652228991474\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of loans in default (ADASYN resampled):  48.56040383751355\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.268787451973964\n",
      "percentage of loans predicted to default (ADASYN):  None\n"
     ]
    }
   ],
   "source": [
    "X_resampled, y_resampled = SMOTE().fit_resample(X, y)\n",
    "print('percentage of loans in default (SMOTE resampled): ', \n",
    "      100 * np.mean(y_resampled == 'default'))\n",
    "\n",
    "full_model = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "full_model.fit(X_resampled, y_resampled)\n",
    "print('percentage of loans predicted to default (SMOTE): ', \n",
    "      100 * np.mean(full_model.predict(X) == 'default'))\n",
    "\n",
    "\n",
    "X_resampled, y_resampled = ADASYN().fit_resample(X, y)\n",
    "print('percentage of loans in default (ADASYN resampled): ', \n",
    "      100 * np.mean(y_resampled == 'default'))\n",
    "\n",
    "full_model = LogisticRegression(penalty=\"l2\", C=1e42, solver='liblinear')\n",
    "full_model.fit(X_resampled, y_resampled)\n",
    "print('percentage of loans predicted to default (ADASYN): ', \n",
    "print(      100 * np.mean(full_model.predict(X) == 'default')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "N/A% (0 of 11) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18% (2 of 11) |####                     | Elapsed Time: 0:00:00 ETA:  00:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36% (4 of 11) |#########                | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 54% (6 of 11) |#############            | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 72% (8 of 11) |##################       | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90% (10 of 11) |#####################   | Elapsed Time: 0:00:00 ETA:   0:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n",
      "   max_iter=100, scale=None, terms=s(0) + s(1) + intercept, \n",
      "   tol=0.0001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "loan3000 = pd.read_csv(LOAN3000_CSV)\n",
    "\n",
    "predictors = ['borrower_score', 'payment_inc_ratio']\n",
    "outcome = 'outcome'\n",
    "\n",
    "X = loan3000[predictors]\n",
    "y = loan3000[outcome]\n",
    "\n",
    "loan_tree = DecisionTreeClassifier(random_state=1, criterion='entropy', \n",
    "                                   min_impurity_decrease=0.003)\n",
    "loan_tree.fit(X, y)\n",
    "\n",
    "loan_lda = LinearDiscriminantAnalysis()\n",
    "loan_lda.fit(X, y)\n",
    "\n",
    "logit_reg = LogisticRegression(penalty=\"l2\", solver='liblinear')\n",
    "logit_reg.fit(X, y)\n",
    "\n",
    "\n",
    "## model\n",
    "gam = LinearGAM(s(0) + s(1))\n",
    "print(gam.gridsearch(X.values, [1 if yi == 'default' else 0 for yi in y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-32-3e4a93a4b77a>:29: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  c = ax.pcolormesh(xx, yy, Z, cmap='Blues', vmin=0.1, vmax=1.3)\n",
      "<ipython-input-32-3e4a93a4b77a>:29: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  c = ax.pcolormesh(xx, yy, Z, cmap='Blues', vmin=0.1, vmax=1.3)\n",
      "<ipython-input-32-3e4a93a4b77a>:29: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  c = ax.pcolormesh(xx, yy, Z, cmap='Blues', vmin=0.1, vmax=1.3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    'Decision Tree': loan_tree,\n",
    "    'Linear Discriminant Analysis': loan_lda,\n",
    "    'Logistic Regression': logit_reg,\n",
    "    'Generalized Additive Model': gam,\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(5, 5))\n",
    "\n",
    "xvalues = np.arange(0.25, 0.73, 0.005)\n",
    "yvalues = np.arange(-0.1, 20.1, 0.1)\n",
    "xx, yy = np.meshgrid(xvalues, yvalues)\n",
    "X = np.c_[xx.ravel(), yy.ravel()]\n",
    "\n",
    "boundary = {}\n",
    "\n",
    "for n, (title, model) in enumerate(models.items()):\n",
    "    ax = axes[n // 2, n % 2]\n",
    "    predict = model.predict(X)\n",
    "    if 'Generalized' in title:\n",
    "        Z = np.array([1 if z > 0.5 else 0 for z in predict])\n",
    "    else:\n",
    "        \n",
    "        Z = np.array([1 if z == 'default' else 0 for z in predict])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    boundary[title] = yvalues[np.argmax(Z > 0, axis=0)]\n",
    "    boundary[title][Z[-1,:] == 0] = yvalues[-1]\n",
    "\n",
    "    c = ax.pcolormesh(xx, yy, Z, cmap='Blues', vmin=0.1, vmax=1.3)\n",
    "    ax.set_title(title)\n",
    "    ax.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boundary['borrower_score'] = xvalues\n",
    "boundaries = pd.DataFrame(boundary)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "boundaries.plot(x='borrower_score', ax=ax)\n",
    "ax.set_ylabel('payment_inc_ratio')\n",
    "ax.set_ylim(0, 20)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
